Data interpretation with noise signal analysis

ABSTRACT

Methods and systems for providing and processing data are disclosed. An example method can comprise determining a first weighted probability based on a probability of occurrence of a noise signal and a first likelihood ratio. The first likelihood ratio is based on a frequency distribution of the noise signal. An example method can comprise determining a second weighted probability based on a probability of non-occurrence of the noise signal and a second likelihood ratio. An example method can comprise determining a combination of the first weighted probability and the second weighted probability, and providing the combination to a decoder configured to decode a value based on the combination.

BACKGROUND

Data signals provided over a physical network are subject to a varietyof transmission problems. These transmission problems can distort theoriginal data signal before the data signal is interpreted at areceiving device. These distortions result in the loss of data. Thus,there is a need for more sophisticated methods and systems forinterpreting data signals on a network link to minimize data loss.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Methods and systems for processingdata are disclosed. An example method can comprise determining aprobability of occurrence of a noise signal on a network link duringtransmission of data. A first probability that the data represents avalue can be determined. The first probability can be based on apresumption of occurrence of the noise signal during transmission of thedata. A second probability that the data represents the value can bedetermined. The second probability can be based on a presumption ofnon-occurrence of the noise signal during transmission of the data. Athird probability that the data represents the value can be determined.The third probability can be based on at least one of the firstprobability weighted by the probability of occurrence of the noisesignal and the second probability weighted by a probability ofnon-occurrence of the noise signal. In one aspect, the data can bedecoded based on the third probability.

In another aspect, an example method can comprise determining a firstweighted probability based on a probability of occurrence of a noisesignal and based on a first likelihood ratio. The first likelihood ratiocan be based on a frequency distribution of the noise signal. A secondweighted probability can be determined based on a probability ofnon-occurrence of the noise signal and a second likelihood ratio. Acombination (e.g., summation, product, division, exponential operationor other combination) of the first weighted probability and the secondweighted probability can be determined. The combination can be providedto a decoder configured to decode a value (e.g., bit value) based on thecombination.

In yet another aspect, an example method can comprise receiving a firstprobability that data comprises a value (e.g., bit value) anddetermining the value based on the first probability. The firstprobability can be based on a function with input values, such as asecond probability and a third probability. The function can be asummation, average, division, multiplication, logarithm and/or otherfunction. For example, the first probability can be based on a weightedsummation of a second probability and a third probability. The secondprobability can comprise a probability that the data comprises thevalue. The second probability can be weighted in the summation based ona probability of occurrence of a noise signal. The third probability cancomprise a probability that the data comprises the value. The thirdprobability can be weighted in the summation based on a probability ofnon-occurrence of the noise signal.

By way of explanation, the present methods and systems can beimplemented by a network device receiving a data transmission. Certainnoise signals, such as impulse noise signals, can be introduced in to adata transmission (e.g., on a hybrid fiber-coax transmission link) atvarious locations along the data transmission path. For example, thedata transmission can comprise an upstream transmission from a user toservice provider (e.g., cable network provider, internet serviceprovider). The noise signal can be generated by one or more electronicdevices, motors, switches, and/or the like at the user location, in thedistribution and/or access network of the service provider, and/or thelike. The present methods and systems can be used to filter out thenoise signal and/or otherwise interpret the data transmission in thepresence of the noise signal.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a block diagram illustrating an example system;

FIG. 2 is a block diagram illustrating an example computing system inwhich the present methods and systems can operate;

FIG. 3 is a block diagram illustrating an example system;

FIG. 4 is a flowchart illustrating an example method;

FIG. 5 is a flowchart illustrating another example method; and

FIG. 6 is a flowchart illustrating yet another example method.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described, itis to be understood that the methods and systems are not limited tospecific methods, specific components, or to particular implementations.It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting.

As used in the specification and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the context clearlydictates otherwise. Ranges may be expressed herein as from “about” oneparticular value, and/or to “about” another particular value. When sucha range is expressed, another embodiment includes from the oneparticular value and/or to the other particular value. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms anotherembodiment. It will be further understood that the endpoints of each ofthe ranges are significant both in relation to the other endpoint, andindependently of the other endpoint.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as “comprising” and“comprises,” means “including but not limited to.” and is not intendedto exclude, for example, other components, integers or steps.“Exemplary” means “an example of” and is not intended to convey anindication of a preferred or ideal embodiment. “Such as” is not used ina restrictive sense, but for explanatory purposes.

Disclosed are components that can be used to perform the disclosedmethods and systems. These and other components are disclosed herein,and it is understood that when combinations, subsets, interactions,groups, etc. of these components are disclosed that while specificreference of each various individual and collective combinations andpermutation of these may not be explicitly disclosed, each isspecifically contemplated and described herein, for all methods andsystems. This applies to all aspects of this application including, butnot limited to, steps in disclosed methods. Thus, if there are a varietyof additional steps that can be performed it is understood that each ofthese additional steps can be performed with any specific embodiment orcombination of embodiments of the disclosed methods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

Embodiments of the methods and systems are described below withreference to block diagrams and flowchart illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

In one aspect, the present methods and systems are related to thetransferring and interpretation of data across a network. Morespecifically, the present methods and systems are related to modulation,demodulation, encoding, decoding, and/or other aspects related totransmitting data in the presence of a noise signal, distortion, and/orother interference on a network. For example, data can be providedacross a network as a data signal to a device. The device can beconfigured to interpret a value that the data signal represents. Forexample, the device can interpret the value based on soft-decodingtechniques, such as low density parity check based decoding. In oneaspect, the device can comprise a demodulator configured to determine aprobability (e.g., log-likelihood ratio) that a portion of a distortedsignal is intended to represent a particular data value. The probabilitycan be based on a probability of whether a noise signal occurred duringtransmission of the data. For example, the probability can be based on asummation of a first probability and a second probability. The firstprobability can be determined based on an assumption that the noisesignal occurred during transmission of the data. The second probabilitycan be determined based on the assumption that the noise signal did notoccur during transmission of the data. The first probability can beweighted in the summation by a probability that the noise signaloccurred during transmission of the data. The second probability can beweighted in the summation by a probability that the noise signal did notoccur during the transmission of the data.

As an illustration, the present methods and systems can be implementedby a network device of a service provider upstream from a user device(e.g., where the noise impulse is generate). The service provider can bea network service provider, cable network provider, content provider,and/or the like. The present methods and systems can be used tointerpret data signals with or without noise on a wireless network,fiber optic network, coaxial network, a combination thereof, and/or thelike.

FIG. 1 illustrates various aspects of an exemplary system in which thepresent methods and systems can operate. Those skilled in the art willappreciate that present methods may be used in systems that employ bothdigital and analog equipment. One skilled in the art will appreciatethat provided herein is a functional description and that the respectivefunctions can be performed by software, hardware, or a combination ofsoftware and hardware.

The system 100 can comprise a central location 101 (e.g., a headend),which can receive content (e.g., data, input programming, and the like)from multiple sources. The central location 101 can combine the contentfrom the various sources and can distribute the content to user (e.g.,subscriber) locations (e.g., location 119) via distribution system 116.

In an aspect, the central location 101 can receive content from avariety of sources 102 a, 102 b, 102 c. The content can be transmittedfrom the source to the central location 101 via a variety oftransmission paths, including wireless (e.g. satellite paths 103 a, 103b) and terrestrial path 104. The central location 101 can also receivecontent from a direct feed source 106 via a direct line 105. Other inputsources can comprise capture devices such as a video camera 109 or aserver 110. The signals provided by the content sources can include asingle content item or a multiplex that includes several content items.

The central location 101 can comprise one or a plurality of receivers111 a, 111 b, 111 c, 111 d that are each associated with an inputsource. For example, MPEG encoders such as encoder 112, are included forencoding local content or a video camera 109 feed. A switch 113 canprovide access to server 110, which can be a Pay-Per-View server, a dataserver, an internet router, a network system, a phone system, and thelike. Some signals may require additional processing, such as signalmultiplexing, prior to being modulated. Such multiplexing can beperformed by multiplexer (mux) 114.

The central location 101 can comprise one or a plurality of modulators115 for interfacing to the distribution system 116. The modulators canconvert the received content into a modulated output signal suitable fortransmission over the distribution system 116. The output signals fromthe modulators can be combined, using equipment such as a combiner 117,for input into the distribution system 116.

A control system 118 can permit a system operator to control and monitorthe functions and performance of system 100. The control system 118 caninterface, monitor, and/or control a variety of functions, including,but not limited to, the channel lineup for the television system,billing for each user, conditional access for content distributed tousers, and the like. Control system 118 can provide input to themodulators for setting operating parameters, such as system specificMPEG table packet organization or conditional access information. Thecontrol system 118 can be located at central location 101 or at a remotelocation.

The distribution system 116 can distribute signals from the centrallocation 101 to user locations, such as user location 119. Thedistribution system 116 can be an optical fiber network, a coaxial cablenetwork, a hybrid fiber-coaxial network, a wireless network, a satellitesystem, a direct broadcast system, or any combination thereof. There canbe a multitude of user locations connected to distribution system 116.At user location 119, a decoder 120, such as a gateway or homecommunications terminal (HCT) can decode, if needed, the signals fordisplay on a display device, such as on a television set (TV) 121 or acomputer monitor. In one aspect, the user location 119 can comprise ademodulator 122 configured to receive and interpret data and/or mediasignals. For example, the demodulator 122 can provide a probability(e.g., likelihood ratio, log-likelihood ratio) that a portion of areceived data and/or media signal represents a value, such as a bitvalue (e.g., 0, 1). Those skilled in the art will appreciate that thesignal can be decoded and/or demodulated in a variety of equipment,including an HCT, a computer, a TV, a monitor, or satellite dish. In anexemplary aspect, the methods and systems disclosed can be locatedwithin, or performed on, one or more HCT's 120, TV's 121, centrallocations 101, DVR's, home theater PC's, and the like.

In an aspect, user location 119 is not fixed. By way of example, a usercan receive content from the distribution system 116 on a mobile devicesuch as a laptop computer, PDA, smartphone, GPS, vehicle entertainmentsystem, portable media player, and the like.

In an exemplary embodiment, the methods and systems disclosed can belocated within equipment located at user location 119 or otherlocations. For example, the methods and systems can be implemented onthe demodulator 122 and/or decoder 120. As a further illustration, themethods and systems disclosed can be used to determine probabilityvalues that one or more (e.g., each) portions of a data signal representa data bit. The probability values can be provided to the decoder 120,and the decoder can determine data values based on the probabilityvalues.

In an exemplary aspect, the methods and systems can be implemented on acomputer 201 as illustrated in FIG. 2 and described below. By way ofexample, server 110 of FIG. 1, the first device 302 of FIG. 3, and/orthe second device 312 of FIG. 3 can be a computer as illustrated in FIG.2. Similarly, the methods and systems disclosed can utilize one or morecomputers to perform one or more functions in one or more locations.FIG. 2 is a block diagram illustrating an exemplary operatingenvironment for performing the disclosed methods. This exemplaryoperating environment is only an example of an operating environment andis not intended to suggest any limitation as to the scope of use orfunctionality of operating environment architecture. Neither should theoperating environment be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 201. The components of thecomputer 201 can comprise, but are not limited to, one or more processor203, a system memory 212, and a system bus 213 that couples varioussystem components including the processor 203 to the system memory 212.In the case of multiple processors 203, the system can utilize parallelcomputing.

The system bus 213 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, sucharchitectures can comprise an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, an AcceleratedGraphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI),a PCI-Express bus, a Personal Computer Memory Card Industry Association(PCMCIA), Universal Serial Bus (USB) and the like. The bus 213, and allbuses specified in this description can also be implemented over a wiredor wireless network connection and each of the subsystems, including theprocessor 203, a mass storage device 204, an operating system 205,signal interpretation software 206, signal interpretation data 207, anetwork adapter 208, system memory 212, an Input/Output Interface 210, adisplay adapter 209, a display device 211, and a human machine interface202, can be contained within one or more remote computing devices 214a,b,c at physically separate locations, connected through buses of thisform, in effect implementing a fully distributed system.

The computer 201 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 201 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 212 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 212 typically contains data such as signal interpretationdata 207 and/or program modules such as operating system 205 and signalinterpretation software 206 that are immediately accessible to and/orare presently operated on by the processor 203.

In another aspect, the computer 201 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 2 illustrates a mass storage device 204 whichcan provide non-volatile storage of computer code, computer readableinstructions, data structures, program modules, and other data for thecomputer 201. For example and not meant to be limiting, a mass storagedevice 204 can be a hard disk, a removable magnetic disk, a removableoptical disk, magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), and thelike.

Optionally, any number of program modules can be stored on the massstorage device 204, including by way of example, an operating system 205and signal interpretation software 206. Each of the operating system 205and signal interpretation software 206 (or some combination thereof) cancomprise elements of the programming and the signal interpretationsoftware 206. Signal interpretation data 207 can also be stored on themass storage device 204. Signal interpretation data 207 can be stored inany of one or more databases known in the art. Examples of suchdatabases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server,Oracle®, mySQL, PostgreSQL, and the like. The databases can becentralized or distributed across multiple systems.

In another aspect, the user can enter commands and information into thecomputer 201 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the processor 203 via a humanmachine interface 202 that is coupled to the system bus 213, but can beconnected by other interface and bus structures, such as a parallelport, game port, an IEEE 1394 Port (also known as a Firewire port), aserial port, or a universal serial bus (USB).

In yet another aspect, a display device 211 can also be connected to thesystem bus 213 via an interface, such as a display adapter 209. It iscontemplated that the computer 201 can have more than one displayadapter 209 and the computer 201 can have more than one display device211. For example, a display device can be a monitor, an LCD (LiquidCrystal Display), or a projector. In addition to the display device 211,other output peripheral devices can comprise components such as speakers(not shown) and a printer (not shown) which can be connected to thecomputer 201 via Input/Output Interface 210. Any step and/or result ofthe methods can be output in any form to an output device. Such outputcan be any form of visual representation, including, but not limited to,textual, graphical, animation, audio, tactile, and the like. The display211 and computer 201 can be part of one device, or separate devices.

The computer 201 can operate in a networked environment using logicalconnections to one or more remote computing devices 214 a,b,c. By way ofexample, a remote computing device can be a personal computer, portablecomputer, smartphone, a server, a router, a network computer, a peerdevice or other common network node, and so on. Logical connectionsbetween the computer 201 and a remote computing device 214 a,b,c can bemade via a network 215, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be througha network adapter 208. A network adapter 208 can be implemented in bothwired and wireless environments. Such networking environments areconventional and commonplace in dwellings, offices, enterprise-widecomputer networks, intranets, and the Internet.

For purposes of illustration, application programs and other executableprogram components such as the operating system 205 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 201, and are executed by the processor 203. Animplementation of signal interpretation software 206 can be stored on ortransmitted across some form of computer readable media. Any of thedisclosed methods can be performed by computer readable instructionsembodied on computer readable media. Computer readable media can be anyavailable media that can be accessed by a computer. By way of exampleand not meant to be limiting, computer readable media can comprise“computer storage media” and “communications media.” “Computer storagemedia” comprise volatile and non-volatile, removable and non-removablemedia implemented in any methods or technology for storage ofinformation such as computer readable instructions, data structures,program modules, or other data. Exemplary computer storage mediacomprises, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by acomputer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

FIG. 3 is a block diagram illustrating an example system 300 forprocessing data. In one aspect, the system 300 can comprise a firstdevice 302. The first device 302 can comprise data 304. For example,data 304 can comprise video, audio, text, metadata, and other content.The data 304 can be organized as one or more data streams, such ascontent channels, video on demand, digital video recordings, and thelike. In one aspect, the first device 302 can be configured to providethe data 304 to other devices through a network 306. For example, thefirst device 300 can be configured as a data server.

In one aspect, the network 306 can comprise a packet switched network(e.g., internet protocol based network), a non-packet switched network(e.g., quadrature amplitude modulation based network), and/or the like.The network 306 can comprise network adapters, switches, routers,modems, and the like connected through wireless links (e.g., radiofrequency, satellite) and/or physical links (e.g., fiber optic cable,coaxial cable, Ethernet cable, or a combination thereof). In one aspect,the network 306 can be configured to provide communication fromtelephone, cellular, modem, and/or other electronic devices to andthroughout the system 300.

In one aspect, the first device 302 can comprise a variety of devicesconfigured to process the data 304. For example, the first device 302can be a converged cable access platform (CCAP) configured to providethe data 304 across a variety of networks in a variety of formats. Inone aspect, the first device 302 can comprise an encoder 308 configuredto encode the data 304. For example, the encoder 308 can compress,encrypt, convert, or otherwise modify the data 304. As an example, theencoder 308 can compress the data 304 to an MPEG based format.

In one aspect, the encoder 308 can be configured to employ one or moreerror correction techniques. For example, the encoder 308 can beconfigured to segment the data into a plurality of codewords. Forexample, the encode 308 can encode a portion of the data as a codeword.The codeword can comprise a plurality of data bits. The encoder 308 canbe configured to generate one or more error check bits (e.g., paritycheck bits) based on the data bits. As an example, the one or more errorcheck bits can be generated by multiplying the data bits by a codewordgeneration matrix. Thus data bits of the codeword can be mathematicallyrelated to the one or more error check bits. For example, the one ormore error check bits can determine linear combinations of data bitsthat equal a specified number (e.g., zero). As a further example, theerror checking bits can specify a plurality of equations configured toevaluate to a certain value when the data bits remain intact (e.g.,through transmission on the network 306). In one aspect, the encoder 308can be configured as a low-density parity check encoder, or otherencoder configured to encode error check bits for a soft-decisiondecoder.

In one aspect, the first device 302 can comprise a modulator 310configured to modulate the data 304 for transmission across the network306. For example, the modulator 310 can be configured to use analogand/or digital modulation, such as amplitude modulation (e.g.,quadrature amplitude modulation), phase-shift keying (e.g., quadraturephase-shift keying), and the like. The modulator 310 can be configuredto convert the data 304 to one or more data signals (e.g., radiofrequency data signals) for transmission across the network 306. In oneaspect, the modulator 310 can be configured to provide the data 304based on one or more multiplexing techniques, such as orthogonalfrequency division multiplexing (OFDM). For example, the modulator 310can be configured to modulate the data 304 as a plurality of datasymbols at a symbol rate. A data symbol can comprise a waveform thatpersists for a given period of time. The symbol rate can define thenumber of symbols provided to a network link of the network 306 by themodulator 310 during a time period. In one aspect, the modulator 310 canbe configured to modulate a data symbol as a plurality of data signalsat one or more subcarrier frequencies. As an example, the modulator 310can map one or more bit values of the codeword to a data vector. In oneaspect, a data vector can be based on an in-phase value (I-value) and aquadrature value (Q-value). For example, the I-value and Q-value candefine two orthogonal components of the data vector. The I-value andQ-value can be mapped or otherwise associated with the one or more bitvalues according to a predefined constellation diagram. The modulator310 can generate a data signal based on the I-value and Q-value. Themodulator 310 can also generate the data signal based on amplitudevalues according to the predefined constellation diagram. In somescenarios, the I-value and Q-value can inherently comprise amplitudevalues. In other scenarios, the amplitude values can be separate fromthe I-value and Q-value. As an illustration, the modulator 310 cangenerate a sinusoidal signal with a particular amplitude, I-value, andQ-value at a subcarrier frequency associated with a symbol. As a furtherexample, the I-value can define a sinusoidal wave component that isshifted in phase (e.g., 90 degrees) from a sinusoidal wave componentdefined by the Q-value.

In one aspect, the system 300 can comprise a second device 312. Thesecond device 312 can be configured to receive data 304 from the firstdevice 302 through the network 306. The second device 312 can beconfigured to process the data 304 and provide the data 304 forconsumption by one or more user (e.g., subscriber, customer). The seconddevice 312 can comprise a set-top box, television, computing device,digital streaming device, gateway, and/or other device configured toreceive a modulated data signal.

In one aspect, the second device 312 can comprise a network analysisunit 314. The network analysis unit 314 can be configured to measureand/or determine the occurrence of one or more noise signals. A noisesignal can comprise an impulse signal, thermal noise signal, and/or thelike. The impulse signal can be caused by an electronic device,electrical motor, light switch, and/or the like. For example, theimpulse signal can have a short time duration (e.g., less than 5microseconds), a different (e.g., high) amplitude in comparison to thedata signal, and occur over a wide range of frequencies (e.g., from 5 to50 MHz).

In one aspect, the network analysis unit 314 can be configured todetermine a probability of occurrence of a noise signal on a networklink (e.g., of the network 306) during transmission of the data 304. Aprobability of occurrence of a noise signal can be determined for one ormore (e.g., each) of a plurality of frequencies, such as subcarrierfrequencies associated with a data signal.

For example, the network analysis unit 314 can be configured todetermine a probability of occurrence of one or more noise signals forone or more radio frequencies of the network link. In one aspect, thenetwork analysis unit 314 can be configured to determine a frequencyresponse (e.g., frequency distribution) of one or more noise signals.The frequency response can comprise a distribution of noise signalamplitudes over a range of frequencies. In one aspect, the range offrequencies can comprise subcarrier frequencies associated withtransmitted data. In one aspect, the network analysis unit 314 can beconfigured to associate a plurality of signal-to-noise ratios with aplurality of respective subcarrier frequencies. The signal-to-noiseratios can be associated with plurality of respective subcarrierfrequencies based on the frequency response of the one or more noisesignals. As an illustration, when a noise signal is not present, adefault signal-to-noise ratio (e.g., 30 dB, 33 dB) can be associatedwith all of the subcarrier frequencies. When a noise signal is presentthe signal-to-noise ratios associated with subcarrier frequencies canvary among the subcarrier frequencies based on the frequency response ofthe noise signal. For example, a noise signal can have a higheramplitude at certain frequencies (e.g., 5 MHz) and a lower amplitude atother frequencies (e.g., 42 MHz). When a noise signal is present thesignal-to-noise ratio can be lower at the frequencies where the noisesignal amplitude is higher. The signal-to-noise ratio can be higher atfrequencies where the noise signal amplitude is lower. As anillustration, the network analysis unit 314 can associate asignal-to-noise ratio of 10 dB with a 5 MHz subcarrier frequency (e.g.,where the noise signal has a high amplitude) and a signal-to-noise ratioof 33 dB with a 42 MHz subcarrier frequency (e.g., where the noisesignal amplitude is low). It should be noted, however, that thesenumbers are merely provided for illustration, and a variety ofsignal-to-noise ratios can be associated with a variety of subcarrierfrequencies according to the particular noise signal.

In one aspect, the network analysis unit 314 can be configured todetermine the probability of occurrence of the noise signal based on aprior history of the noise signal occurrences (e.g., from one or moresources). For example, the network analysis unit 314 can be configuredto determine a history of occurrence of the noise signal on a networklink of the network 306 over a previous time interval. As a furtherillustration, the noise signal can comprise a recurring pattern ofsignals. For example, the noise signal can comprise a group of two ormore peaks, impulses, and/or other features. Such features can be spacedby a first interval. The group can reoccur according to a secondinterval. As an illustration, the group of features can comprise 4impulses that occur 400 microseconds part. The example group can reoccurevery 9 milliseconds. It should be noted, however, that a noise signalcan comprise a variety of features spaced at a variety of timeintervals.

In one aspect, the network analysis unit 314 can be configured todetermine the probability of occurrence of the noise signal based on aprediction of the occurrence of the noise signal. For example, theprediction can be based on the previous occurrence of at least a portionof the recurring pattern or other characteristic of the noise signal.For example, if a noise signal was detected at a first time, the networkanalysis unit 314 can be configured to predict that the noise signalwill occur again at a second time. The second time can be determinedbased on the first interval, second interval, the first intervalmultiplied by a factor, the second interval multiplied by a factor, anycombination thereof, and/or the like added to the first time.

In one aspect, the network analysis unit 314 can be configured todetermine the probability of occurrence of the noise signal based on adetected signal. For example, the network analysis unit 314 can detect asignal and determine a probability of whether the signal comprises anoise signal. The signal can be detected during the time interval overwhich the data signal is transmitted. In one aspect, the signal can bedetected based on a characteristic portion of the noise signal. Forexample, the characteristic portion of the noise signal can comprise acharacteristic amplitude. The characteristic amplitude can be higherthan an amplitude expected from the data signal. For example, thecharacteristic amplitude can be detected at a subcarrier frequency atwhich the data signal is nulled. As a further example, thecharacteristic amplitude can be detected at a time between transmissionof symbols of the data signal. As another example, if a portion of thenoise signal occurs during transmission of a cyclic prefix of a symbolof the data signal, then the characteristic amplitude can be detected bycomparison of an expected amplitude of the cyclic prefix. In one aspect,the network analysis unit 314 can detect a noise signal based on apattern on a constellation diagram. For example, a noise signal can beindicated if a constellation diagram shows a circle around an expectedpoint on the constellation diagram.

The network analysis unit 314 can be configured to determine theprobability of occurrence of the noise signal based on a predefineddistribution. The predefined distribution can be selected based on thepredefined distribution matching (e.g., within a threshold) or otherwisecomprising characteristics similar to the noise signal. In somescenarios, the noise signal may not exhibit deterministic behavior, suchas recurring patterns. In such scenario or otherwise, the predefineddistribution can be selected as a basis for determining and/orpredicting the probability of occurrence of the noise signal. In oneaspect, the probability of occurrence of the noise signal can be basedon the mapping of a history of a noise signal to the predefineddistribution. For example, the predefined distribution can be used topredict when the noise signal will occur during a time interval. Theprediction by the predefined distribution can be based on the history ofthe noise signal. In one aspect, the predefined distribution can bebased on a parameter, such as a rate parameter and/or time parameterthat is based on the history of the noise signal. As an example, mappingthe history of the noise signal to the predefined distribution cancomprise determining a measured or otherwise known average rate ofoccurrence of a noise signal (e.g., impulse noise) during a given timeperiod (e.g., useful symbol time) and determining the predefineddistribution by applying the average rate of occurrence of the noisesignal to a function configured to generate the predefined distribution.As a further example, mapping the history of the noise signal to thepredefined distribution can comprise determining a parameter based onthe history of the noise signal and determining a probability that thenoise signal will occur in a given time period by applying the parameterin the generation of a predefined distribution.

In one aspect, the predefined distribution can comprise a Poissondistribution as follows:

$\begin{matrix}{{P\left( {X = k} \right)} = {{\mathbb{e}}^{- \lambda} \cdot \frac{\lambda^{k}}{k!}}} & \left( {{Eq}.\mspace{14mu} 1} \right) \\{{P\left\{ {{{X\left( {s + t} \right)} - {X(s)}} = k} \right\}} = {\left( {\lambda \cdot t} \right)^{k} \cdot \frac{{\mathbb{e}}^{{- \lambda} \cdot t}}{k!}}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$where P(X=k) signifies the Poisson distribution function, lambda (λ)signifies a rate parameter, t signifies a time parameter, X is a randomvariable representing the number of occurrences in a unit interval(e.g., a time interval), s represents a point in time, and k is avariable representing a number of impulses. In one aspect, the lambdaparameter can be determined based on the number of impulses observedover a period of time on a network link. For example, the lambdaparameter can be determined by dividing the number of impulses observedby the time duration of the observation. As an illustration, an examplenoise signal can be an impulse signal harmonically related to a 60 Hzpower line alternating current (AC) rate. The example noise signal canbe observed every 8 milliseconds on average. The probability that thereare k impulses (e.g., for k=0, 1, 2, 3) can be determined by insertingthe measured rate parameter into equation (1). In another aspect, theprobability of k impulses during a given time period can be determinedbased on equation (2). For example, equation (2) can determine theprobability of k impulses at time s over a time interval of t seconds.As an illustration, given a rate parameter, the probability that one ormore impulses or other noise signals will occur during transmission of a40 microseconds symbol (e.g., such as an OFDM symbol) can determined. Itshould be noted that other predefined distributions can be used insteadof a Poisson distribution, such as a Gaussian distribution.

In one aspect, the second device 312 can comprise a demodulator 316. Inone aspect, the demodulator 316 can be configured to demodulate data 304that is transmitted across the network 306. The demodulator 316 can beconfigured to receive modulated signals representative of data bits ofthe data 304 and associated error check bits. In one aspect, thedemodulator 316 can be configured to determine a value for each data bitaccording to at least a portion of a data signal received by thedemodulator 316. For example, the demodulator 316 can be configured todetermine an amplitude, I-value, Q-value, and/or the like of a receiveddata signal at a subcarrier frequency. The demodulator 316 can beconfigured to determine a probability that the data signal (e.g., and/orcorresponding amplitude, I-value, and Q-value) represents a data value.As an explanation, the data signal can be distorted by noise signals orother interference as the signal travels across the network 306.Accordingly, the data signal received by the second device 312 can bedifferent than the original signal transmitted from the first device 302as a result of inclusion of the noise signal or other interference tothe original signal.

In one aspect, the probability that the data signal represents a datavalue can be a likelihood ratio. A likelihood ratio can comprise a ratioof a probability that a signal represents a first value (e.g., 1) and aprobability that the signal represents a second value (e.g., 0). As afurther explanation, a likelihood ratio can comprise a log-likelihoodratio. For example, a log-likelihood ratio can comprise the value of alogarithm function (e.g., natural log) applied to the likelihood ratio.

In one aspect, the probability value can be based on a function withinput values, such as one or more probability values. The function canbe a summation, average, division, multiplication, logarithm and/orother function. In one aspect, the probability value can be based on asummation of two or more probability values. The two or more probabilityvalues can be weighted probability values, such as weighted likelihoodratios. For example, the demodulator 316 can comprise a firstprobability unit 318. The first probability unit 318 can determine afirst probability that a received data signal (e.g., or portion thereof)represents a bit value, such as one or zero. The first probability unit316 can be configured to determine the first probability by summing oneor more other probabilities, such as a second probability and a thirdprobability. The second probability and the third probability can beweighted in the summation. The second probability can be weighted by theprobability of occurrence of the noise signal. The third probability canbe weighted by a probability of non-occurrence of the noise signal. Forexample, the first probability unit 318 can receive the probability ofoccurrence of the noise signal from the network analysis unit 318. Thefirst probability unit 318 can also receive a probability ofnon-occurrence from the network analysis unit 314. In some scenarios,the first probability unit 318 and/or network analysis unit 314 can beconfigured to determine the probability of non-occurrence of the noisesignal based on the probability of occurrence of the noise signal, andvice versa.

In one aspect, the first probability can be specific to a portion of thedata signal provided at specific subcarrier frequency of the datasignal. For example, a plurality of first probabilities can bedetermined for a plurality of portions of the data signal provided atdifferent subcarrier frequencies. As an illustration, the firstprobability can be specific to each subcarrier frequency. For example,the first probability can be determined based on assumption of asignal-to-noise ratio associated with the subcarrier frequency.

In one aspect, the demodulator 316 can comprise a second probabilityunit 320 configured to determine the second probability. The secondprobability can be a probability that the data signal represents a bitvalue, such as a first bit value. The second probability can be based ona presumption of occurrence of a noise signal, such as a first noisesignal, during transmission of the data across at least a portion of thenetwork 306. The second probability can be specific to a portion of thedata signal provided at a specific subcarrier frequency. For example,the second probability can be determined based on a specificsignal-to-noise ratio associated with the specific subcarrier frequency(e.g., based on the frequency response of the noise signal).

In one aspect, the demodulator 316 can also comprise a third probabilityunit 322 configured to determine the third probability. The thirdprobability can be a probability that the data signal represents a bitvalue, such as the first bit value. The third probability can be basedon a presumption of non-occurrence of a noise signal, such as the firstnoise signal, during transmission of the data across at least a portionof the network 306. The third probability can be determined based on aspecified signal-to-noise ratio. For example, one or more defaultsignal-to-noise ratios can be used to determine the third probabilityfor a portion of the data signal provided at a specific subcarrierfrequency. For example, the one or more default signal-to-noise ratioscan be used to determine the third probability for some or all of theportions of the data signal provided at a specific subcarrier.

As an illustration, the first probability can be determined according tothe following equation:Prob{bit=0|{right arrow over (y)}}=Prob{bit=0|{right arrow over(y)}&impulse}*Prob{impulse}+Prob{bit=0|{right arrow over (y)}&noimpulse}*(1−Prob{impulse})  (Eq. 3)The first term (e.g., left of the equal sign) can be the firstprobability. For example, the first probability can comprise aprobability that a data vector “y” represents a bit value of zero. Thesecond term can be the second probability multiplied by the probabilityof occurrence of an impulse noise signal. For example, the secondprobability can comprise a probability that a data vector “y,” which isassumed to be modified by an impulse noise signal, represents a bitvalue of zero. The third term (e.g., after the + sign) can be the thirdprobability multiplied (e.g., weighted) by a probability ofnon-occurrence of an impulse noise signal. For example, the thirdprobability can comprise a probability that a data vector “y,” which isassumed not to be modified by a noise signal, represents a bit value ofzero.

In one aspect, the second device 312 can comprise a decoder 324. Thedecoder can comprise a soft-decision decoder configured to determine thevalues of a plurality of data bits based on probability valuesassociated with data bits. In one aspect, the decoder 324 can comprise alow density parity check decoder. For example, the decoder 324 can beconfigured to receive at least one of the first probability, secondprobability, and third probability from the demodulator 316. As anillustration, the decoder 324 can be configured to determine the valueof data bit based on the first probability. The decoder 324 can beconfigured to receive a respective first probability corresponding toeach (or at least one) data bit value of received data.

In one aspect, the decoder 324 can be configured to determine data bitvalues of the received data signal based on at least one probabilityvalue (e.g., a first probability value) representative of an error checkbit (e.g., parity check bit). For example, the decoder 324 can beconfigured to perform an iterative error checking process based on theat least one probability value. As a further example, the decoder 324can use a message passing algorithm to iteratively converge on adetermination (e.g., or approximation) of the data bit values of atransmitted codeword.

FIG. 4 is a flowchart illustrating an example method 400 (e.g., forprocessing data). At step 402, a frequency response of a noise signalcan be determined. For example, the noise signal can comprise an impulsesignal, thermal noise signal, and/or the like. The impulse signal can becaused by an electronic device, electrical motor, light switch, and/orthe like. For example, the impulse signal can have a short time duration(e.g., less than 5 microseconds), a different (e.g., high) amplitude incomparison to the data signal, and occur over a wide range offrequencies (e.g., from 5 to 50 MHz). In one aspect, step 402 cancomprise associating at least one signal-to-noise ratio with at leastone frequency of the noise signal. In another aspect, step 402 can bebased on a measurement of the frequency response. For example, lowersignal-to-noise ratios can be associated with frequencies at which theamplitude of a noise signal has increased, and higher signal-to-noiseratios can be associated with frequencies at which the amplitude ofnoise signal has decreased.

At step 404, a signal-to-noise ratio can be associated with data basedon the frequency response. For example, step 404 can comprisedetermining a frequency associated with the data (e.g., frequency atwhich the data was received). Step 404 can comprise selecting asignal-to-noise ratio associated with the frequency associated with thedata. For example, the selected signal-to-noise ratio can be associatedwith the frequency based on the frequency response of the noise signal.The data can comprise data received across a network link. The data canrepresent a bit value of a data signal.

At step 406, a probability of occurrence of a noise signal on a networklink during transmission of the data can be determined. For example,step 406 can comprise determining a history of occurrence of the noisesignal on the network link over a previous time interval and mapping thehistory to a Poisson distribution, Gaussian distribution, and/or otherdistribution. Mapping the history of occurrence to the Poissondistribution can comprise determining a parameter (e.g., rate parameter,time parameter) based on the history of occurrence. Mapping the historyof occurrence can comprise applying (e.g., inserting a value) theparameter to a function configured to generate the Poisson distributionbased on the parameter. As another example, step 406 can comprisedetecting a signal and determining a probability of whether the signalcomprises the noise signal. For example, a feature of signal can beidentified as characteristic of a noise signal. The feature can comprisean amplitude value (e.g., a given frequency, during the cyclic prefix),a recurring pattern, and/or the like. As yet another example, step 406can comprise predicting the occurrence of the noise signal based on arecurring pattern of the noise signal. An example pattern can comprise asequence of impulse signals separated by a first time interval. Thesequence can be repeated after a second time interval.

At step 408, a first probability that the data represents a value can bedetermined. The first probability can be based on a presumption ofoccurrence of the noise signal during transmission of the data. Thefirst probability can be based on the signal-to-noise ratio. Forexample, the first probability can be determined based on asignal-to-noise ratio associated with a portion of a noise signal. As afurther example, the signal-to-noise ratio can be selected based on thefrequency distribution. As another example, the signal-to-noise ratiocan be selected because the signal-to-noise ratio is associated with afrequency (e.g., subcarrier frequency of a symbol of a data signal) atwhich the data was received. In one aspect, step 408 can comprisedetermining a probability that the data represents at least one of afirst bit value (e.g., 0) and a second bit value (e.g., 1).

At step 410, a second probability that the data represents the value canbe determined. The second probability can be based on a presumption ofnon-occurrence of the noise signal during transmission of the data. Forexample, the second probability can be determined based on a defaultsignal-to-noise ratio. The default signal-to-noise ratio can be selectedbased on an assumption of non-occurrence of a noise signal.

At step 412, a third probability that the data represents the value canbe determined. The third probability can be based on a function withinput values, such as the first probability and second probability. Thefunction can be a summation, average, division, multiplication,logarithm and/or other function. In one aspect, the third probabilitycan be based on at least one of the first probability weighted by theprobability of occurrence of the noise signal and the second probabilityweighted by a probability of non-occurrence of the noise signal. Forexample, step 412 can comprise summing the first probability weighted bythe probability of occurrence of the noise signal and the secondprobability weighted by the probability of non-occurrence of the noisesignal.

In one aspect, the probabilities determined herein can compriselikelihood ratios. A likelihood ratio can comprise a ratio of aprobability that a value equals a first value (e.g., 1) and aprobability that a value equals a second value (e.g., 0). As a furtherexplanation, a likelihood ratio can comprise a log-likelihood ratio. Forexample a logarithm function can be applied to the likelihood ratio. Asan illustration, the first probability can comprises a first likelihoodratio. The second probability can comprise a second likelihood ratio.The third probability can comprise a third likelihood ratio. Forexample, the third likelihood ratio can be the summation of firstlikelihood ratio weighted by the probability of occurrence of the noisesignal and the second likelihood ratio weighted by the probability ofnon-occurrence of the noise signal.

At step 414, the data can be decoded based on the third probability. Asan example, step 414 can comprise decoding the data with a soft-decisiondecoder, such as low-density parity-check decoder, configured to decodeddata based on probability values.

FIG. 5 is a flowchart illustrating another example method 500 (e.g., forprocessing data). At step 502, a frequency distribution of a noisesignal can be determined. The noise signal can comprise an impulsesignal, thermal noise signal, and/or the like. The impulse signal can becaused by an electronic device, electrical motor, light switch, and/orthe like. For example, the impulse signal can have a short time duration(e.g., less than 5 microseconds), a different (e.g., high) amplitude incomparison to the data signal, and occur over a wide range offrequencies (e.g., from 5 to 50 MHz). For example, step 502 can compriseassociating at least one signal-to-noise ratio with at least onefrequency of the noise signal. For example, lower signal-to-noise ratioscan be associated with frequencies at which the amplitude of a noisesignal has increased, and higher signal-to-noise ratios can beassociated with frequencies at which the amplitude of noise signal hasdecreased. As another example, step 502 can be based on a measurement ofthe noise signal across a range of frequencies.

At step 504, a signal-to-noise ratio can be associated with datarepresentative of a value (e.g., bit value) based on the frequencydistribution. For example, step 504 can comprise determining a frequencyassociated with the data (e.g., frequency at which the data wasreceived). Step 504 can comprise selecting a signal-to-noise ratioassociated with the frequency associated with the data. For example, theselected signal-to-noise ratio can be associated with the frequencybased on the frequency response of the noise signal.

At step 506, the probability of occurrence of the noise signal can bedetermined. For example, step 506 can comprise determining a history ofoccurrence of the noise signal on the network link over a previous timeinterval and mapping the history to a Poisson distribution, Gaussiandistribution, and/or other distribution. Mapping the history ofoccurrence to the Poisson distribution can comprise determining aparameter (e.g., rate parameter, time parameter) based on the history ofoccurrence. Mapping the history of occurrence can comprise applying(e.g., inserting a value) the parameter to a function configured togenerate the Poisson distribution based on the parameter. As anotherexample, step 506 can comprise detecting a signal and determining aprobability of whether the signal comprises the noise signal. Forexample, a feature of signal can be identified as characteristic of anoise signal. The feature can comprise an amplitude value (e.g., a givenfrequency, during the cyclic prefix), a recurring pattern, and/or thelike. As yet another example, step 506 can comprise predicting theoccurrence of the noise signal based on a recurring pattern of the noisesignal. An example pattern can comprise a sequence of impulse signalsseparated by a first time interval. The sequence can be repeated after asecond time interval.

At step 508, a first weighted probability can be determined based on aprobability of occurrence of a noise signal and based on a firstlikelihood ratio. As an example, the first likelihood ratio can be basedon a frequency distribution of the noise signal. As another example, thefirst likelihood ratio can be based on the signal-to-noise ratio. Alikelihood ratio can comprise a ratio of a probability that a valueequals a first bit value (e.g., 1) and a probability that a value equalsa second bit value (e.g., 0). As a further explanation, a likelihoodratio can comprise a log-likelihood ratio. For example a logarithmfunction (e.g., natural logarithm) can be applied to the firstlikelihood ratio. For example, the first likelihood ratio can comprise afirst probability that the value is at least one of a first bit valueand a second bit value. In one aspect, the first weighted probabilitycan comprise a probability (e.g., first likelihood ratio) weighted by(e.g. multiplied by) the probability of occurrence of the noise signal.

In one aspect, the first weighted probability can be determined based ona signal-to-noise ratio associated with a portion of a noise signal. Asa further example, the signal-to-noise ratio can be selected based onthe frequency distribution. As another example, the signal-to-noiseratio can be selected because the signal-to-noise ratio is associatedwith a frequency (e.g., subcarrier frequency of a symbol of a datasignal) at which the data was received.

At step 510, a second weighted probability can be determined based on aprobability of non-occurrence of the noise signal and a secondlikelihood ratio. The second likelihood ratio can comprise a secondprobability that the value is at least one of the first bit value andthe second bit value. In one aspect, the second weighted probability cancomprise a probability (e.g., second likelihood ratio) weighted by (e.g.multiplied by) the probability of non-occurrence of the noise signal. Inone aspect, the second probability can be determined based on a defaultsignal-to-noise ratio. The default signal-to-noise ratio can be selectedbased on an assumption of non-occurrence of a noise signal.

At step 512, a combination (e.g., summation, product, division,exponential operation or other combination) of the first weightedprobability and the second weighted probability can be determined. Forexample, the first weighted probability can be added to the secondweighted probability.

At step 514, the combination can be provided to a decoder configured todecode a value based on the combination. For example, the decoder cancomprise a low-density parity-check decoder. The decoder can compriseany other soft decision encoder. For example, a soft decision decodercan be configured to decode the data based on probability values, suchas likelihood ratios. In one aspect, soft decision encoders can beconfigured to decode data that has been encoded with an error correctingcode.

FIG. 6 is a flowchart illustrating yet another example method 600 (e.g.,for processing data). At step 602, a first probability that datacomprises a value (e.g., bit value) can be received. In one aspect, thefirst probability can be based on a weighted combination (e.g.,summation, product, division, exponential operation or othercombination) of a second probability and a third probability. The secondprobability can be a probability that the data comprises the value. Forexample, the second probability can be based on an assumption ofoccurrence of a noise signal during transmission of the data. The secondprobability can be weighted in the combination based on a probability ofoccurrence of a noise signal. The third probability can be a probabilitythat the data comprises the value. For example, the third probabilitycan be based on an assumption of non-occurrence of a noise signal duringtransmission of the data. The third probability can be weighted in thecombination based on a probability of non-occurrence of the noisesignal.

The noise signal can comprise an impulse signal, thermal noise signal,and/or the like. The impulse signal can be caused by an electronicdevice, electrical motor, light switch, and/or the like. For example,the impulse signal can have a short time duration (e.g., less than 5microseconds), a different (e.g., high) amplitude in comparison to thedata signal, and occur over a wide range of frequencies (e.g., from 5 to50 MHz).

For example, the probability of occurrence of the noise signal can bebased on a mapping of a history of occurrence to a Poisson distribution,Gaussian distribution, and/or other distribution. The history ofoccurrence can comprise a history of occurrence of the noise signal onthe network link over a previous time interval. Mapping the history ofoccurrence to the Poisson distribution can comprise determining aparameter (e.g., rate parameter, time parameter) based on the history ofoccurrence. Mapping the history of occurrence can comprise applying(e.g., inserting a value) the parameter to a function configured togenerate the Poisson distribution based on the parameter. As anotherexample, the probability of occurrence of the noise signal can be basedon a probability of whether a detected signal comprises the noisesignal. For example, a feature of signal can be identified ascharacteristic of a noise signal. The feature can comprise an amplitudevalue (e.g., a given frequency, during the cyclic prefix), a recurringpattern, and/or the like. As a further example, the probability ofoccurrence of the noise signal can be based on a recurring pattern ofthe noise signal. An example pattern can comprise a sequence of impulsesignals separated by a first time interval. The sequence can be repeatedafter a second time interval.

In another aspect, the second probability can be based on asignal-to-noise ratio associated with the data according to a frequencyresponse of the noise signal. The frequency response of the noise signalcan be based on a measurement of the frequency response.

In one aspect, step 602 can comprise determining a probability that thedata represents at least one of a first value (e.g., first bit value)and a second value (e.g., second bit value). In one aspect, theprobabilities determined herein can comprise likelihood ratios. Alikelihood ratio can comprise a ratio of a probability that a value(e.g., bit value) equals a first value (e.g., 1) and a probability thata value equals a second value (e.g., 0). As a further explanation, alikelihood ratio can comprise a log-likelihood ratio. For example alogarithm function can applied to the likelihood ratio. For example, thefirst probability can comprise a first likelihood ratio. The secondprobability can comprise a second likelihood ratio. The thirdprobability can comprise a third likelihood ratio. The first likelihoodratio can be based on a weighted combination (e.g., summation, product,division, exponential operation or other combination) of the secondlikelihood ratio and the third likelihood ratio.

In one aspect of step 602, the second probability can be received (e.g.,by a decoder, such as a soft-decision encoder). The third probabilitycan be received (e.g., by a decoder, such as a soft-decision encoder).The first probability can be determined based on the second probabilityand third probability. For example, a weighted combination (e.g.,summation, product, division, exponential operation or othercombination) of the second probability and third probability can beperformed (e.g., by the decoder, such as a soft-decision encoder). As anexample, step 602 can comprise summing the second probability and thethird probability. The second probability can be weighted in thecombination based on a probability of occurrence of a noise signal. Thethird probability can be weighted in the combination based on aprobability of non-occurrence of the noise signal.

At step 604, the value can be determined based on the first probability.The value can also be determined based on at least one error checkingbit. For example, the value can be decoded with a soft-decision decoder,such as a low-density parity-check decoder.

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is no way intended thatan order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A method, comprising: determining a probabilityof occurrence of a noise signal on a network link during transmission ofdata; determining a first probability that the data represents a value,wherein the first probability is based on a presumption of occurrence ofthe noise signal during transmission of the data; determining a secondprobability that the data represents the value, wherein the secondprobability is based on a presumption of non-occurrence of the noisesignal during transmission of the data; determining a third probabilitythat the data represents the value, wherein the third probability isbased on at least one of the first probability weighted by theprobability of occurrence of the noise signal and the second probabilityweighted by a probability of non-occurrence of the noise signal; anddecoding the data based on the third probability.
 2. The method of claim1, wherein determining the probability of occurrence of the noise signalcomprises determining a history of occurrence of the noise signal on thenetwork link over a previous time interval and mapping the history to aPoisson distribution.
 3. The method of claim 1, wherein determining theprobability of occurrence of the noise signal comprises detecting asignal and determining a probability of whether the signal comprises thenoise signal.
 4. The method of claim 1, wherein determining theprobability of occurrence of the noise signal comprises predicting theoccurrence of the noise signal based on a recurring pattern of the noisesignal.
 5. The method of claim 1, further comprising: determining afrequency response of the noise signal; and associating asignal-to-noise ratio with the data based on the frequency response,wherein the first probability is based on the signal-to-noise ratio. 6.The method of claim 5, wherein determining the frequency response of thenoise signal comprises associating at least one signal-to-noise ratiowith at least one frequency of the noise signal.
 7. The method of claim1, wherein determining the third probability that the data representsthe value comprises summing the first probability weighted by theprobability of occurrence of the noise signal and the second probabilityweighted by the probability of non-occurrence of the noise signal.
 8. Amethod, comprising: determining a first weighted probability based on aprobability of occurrence of a noise signal and a first likelihoodratio, wherein the first likelihood ratio is based on a frequencydistribution of the noise signal; determining a second weightedprobability based on a probability of non-occurrence of the noise signaland a second likelihood ratio; determining a combination of the firstweighted probability and the second weighted probability; and providingthe combination to a decoder configured to decode a value based on thecombination.
 9. The method of claim 8, further comprising determiningthe probability of occurrence of the noise signal.
 10. The method ofclaim 9, wherein determining the probability of occurrence of the noisesignal comprises determining a history of occurrence of the noise signalon a network link over a previous time interval and mapping the historyof occurrence to a Poisson distribution.
 11. The method of claim 9,wherein determining the probability of occurrence of the noise signalcomprises detecting a signal and determining a probability of whetherthe signal comprises the noise signal.
 12. The method of claim 9,wherein determining the probability of occurrence of the noise signalcomprises predicting the occurrence of the noise signal based on arecurring pattern of the noise signal.
 13. The method of claim 8,further comprising: determining the frequency distribution of the noisesignal; and associating a signal-to-noise ratio with data representativeof the value based on the frequency distribution, wherein the firstlikelihood ratio is based on the signal-to-noise ratio.
 14. The methodof claim 8, wherein the decoder comprises a low-density parity-checkdecoder, and wherein the noise signal comprises an impulse signal.
 15. Amethod, comprising: receiving, at a computing device in a network, afirst probability that data comprises a value, wherein the firstprobability is based on a weighted combination of a second probabilityand a third probability, and wherein the second probability is aprobability that the data comprises the value and the second probabilityis weighted in the weighted combination based on a probability ofoccurrence of a noise signal in the network, and wherein the thirdprobability is a probability that the data comprises the value and thethird probability is weighted in the weighted combination based on aprobability of non-occurrence of the noise signal in the network; anddetermining, by the computing device, the value based on the firstprobability.
 16. The method of claim 15, wherein the probability ofoccurrence of the noise signal is based on a mapping of a history ofoccurrence to a Poisson distribution, and wherein the history ofoccurrence is a history of occurrence of the noise signal on the networkover a previous time interval.
 17. The method of claim 15, wherein theprobability of occurrence of the noise signal is based on a probabilityof whether a detected signal comprises the noise signal.
 18. The methodof claim 15, wherein the probability of occurrence of the noise signalis based on a recurring pattern of the noise signal.
 19. The method ofclaim 15, wherein the second probability is based on a signal-to-noiseratio associated with the data according to a frequency response of thenoise signal, and wherein the value is determined based on at least oneerror checking bit.
 20. The method of claim 19, wherein the frequencyresponse of the noise signal is based on a measurement of the frequencyresponse.